Posts Categorized: Machine Learning

From Synonyms to Object Properties It’s well known that word embeddings are excellent for finding similarities between words — specifically, synonyms. We achieve this using supervised machine learning techniques by showing a neural net a dataset of hundreds of millions of pieces of text. The algorithm looks at the context and frequency in which particular… Read more

At the recent VB Summit in Berkeley, Jeff Dean, Head of Google Brain discussed a popular challenge in making Deep Learning a practical solution inside the enterprise: “I would say pretty much any business that has tens or hundreds of thousands of customer interactions has enough scale to start thinking about using these sorts of… Read more

In speech and writing, how often do we use one term — and only that term — to describe an idea? For example, if you were searching through a document for information relating to a business’ current assets, looking up only “current assets” would mean that you miss out on anything discussing cash, short-term assets,… Read more

I recently stumbled across an old Data Science Stack Exchange answer of mine on the topic of the “Best Python library for neural networks”, and it struck me how much the Python deep learning ecosystem has evolved over the course of the past 2.5 years. The library I recommended in July 2014, pylearn2, is no… Read more

Last week Nathan Lintz held a workshop at the Boston Machine Learning Meetup on the basics of TensorFlow. Video and slides below. Overview TensorFlow is a wonderful tool for rapidly implementing neural networks. In this presentation, we will learn the basics of TensorFlow and show how neural networks can be built with just a few… Read more

Here we introduce optical character recognition as a common benchmark task in modern machine learning, and show how to implement a simple model. Being able to experiment with machine learning models is the first step towards capability! Scientific learning process –> machine learning process In the previous post, we introduced machine learning as a principled… Read more

Online clothing stores typically recommend products by looking at their customers’ past purchases or searches, and then suggest items that look similar to those products. Perhaps that’s a good strategy when you’ve only been searching for, say, a striped shirt and haven’t bought one yet. But what if you’ve just bought a striped shirt —… Read more

Transfer learning is one of the most powerful capabilities in the deep learning toolkit because you only need “small data” as opposed to “Big Data”. It’s a technique that allows us to take a deep neural network trained to solve one task (like recognizing objects and logos in an image), and efficiently tweak it to perform another task,… Read more

Earlier this month, Dan gave a talk at Sentiment Analysis Symposium discussing why businesses should consider adopting deep learning solutions. His slides and a video of the presentation are available for those of you who couldn’t make it — if you have any questions, click the little chat bubble at the bottom right hand corner… Read more

In this case study, we evaluate four different strategies for solving a problem with machine learning. In terms of both technical performance and practical factors like economics and amount of training data required, customized models built from semi-supervised “deep” features using transfer learning outperform models built from scratch, and rival state-of-the-art methods. Featured on KDnuggets.… Read more